Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations517
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)0.8%
Total size in memory52.6 KiB
Average record size in memory104.3 B

Variable types

Numeric11
Categorical2

Alerts

Dataset has 4 (0.8%) duplicate rowsDuplicates
DC is highly overall correlated with DMC and 1 other fieldsHigh correlation
DMC is highly overall correlated with DC and 2 other fieldsHigh correlation
FFMC is highly overall correlated with DMC and 2 other fieldsHigh correlation
ISI is highly overall correlated with FFMCHigh correlation
RH is highly overall correlated with tempHigh correlation
month is highly overall correlated with DCHigh correlation
temp is highly overall correlated with DMC and 2 other fieldsHigh correlation
rain has 509 (98.5%) zerosZeros
area has 247 (47.8%) zerosZeros

Reproduction

Analysis started2024-09-24 20:38:05.562442
Analysis finished2024-09-24 20:38:18.736859
Duration13.17 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

X
Real number (ℝ)

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6692456
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:18.834871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3137778
Coefficient of variation (CV)0.49553568
Kurtosis-1.1723308
Mean4.6692456
Median Absolute Deviation (MAD)2
Skewness0.036245822
Sum2414
Variance5.3535678
MonotonicityNot monotonic
2024-09-24T17:38:18.952463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 91
17.6%
6 86
16.6%
2 73
14.1%
8 61
11.8%
7 60
11.6%
3 55
10.6%
1 48
9.3%
5 30
 
5.8%
9 13
 
2.5%
ValueCountFrequency (%)
1 48
9.3%
2 73
14.1%
3 55
10.6%
4 91
17.6%
5 30
 
5.8%
6 86
16.6%
7 60
11.6%
8 61
11.8%
9 13
 
2.5%
ValueCountFrequency (%)
9 13
 
2.5%
8 61
11.8%
7 60
11.6%
6 86
16.6%
5 30
 
5.8%
4 91
17.6%
3 55
10.6%
2 73
14.1%
1 48
9.3%

Y
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2998066
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:19.073469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median4
Q35
95-th percentile6
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2299004
Coefficient of variation (CV)0.28603622
Kurtosis1.4205534
Mean4.2998066
Median Absolute Deviation (MAD)1
Skewness0.41729625
Sum2223
Variance1.512655
MonotonicityNot monotonic
2024-09-24T17:38:19.185497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 203
39.3%
5 125
24.2%
6 74
 
14.3%
3 64
 
12.4%
2 44
 
8.5%
9 6
 
1.2%
8 1
 
0.2%
ValueCountFrequency (%)
2 44
 
8.5%
3 64
 
12.4%
4 203
39.3%
5 125
24.2%
6 74
 
14.3%
8 1
 
0.2%
9 6
 
1.2%
ValueCountFrequency (%)
9 6
 
1.2%
8 1
 
0.2%
6 74
 
14.3%
5 125
24.2%
4 203
39.3%
3 64
 
12.4%
2 44
 
8.5%

month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
aug
184 
sep
172 
mar
54 
jul
32 
feb
20 
Other values (7)
55 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowmar
2nd rowoct
3rd rowoct
4th rowmar
5th rowmar

Common Values

ValueCountFrequency (%)
aug 184
35.6%
sep 172
33.3%
mar 54
 
10.4%
jul 32
 
6.2%
feb 20
 
3.9%
jun 17
 
3.3%
oct 15
 
2.9%
apr 9
 
1.7%
dec 9
 
1.7%
jan 2
 
0.4%
Other values (2) 3
 
0.6%

Length

2024-09-24T17:38:19.423189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aug 184
35.6%
sep 172
33.3%
mar 54
 
10.4%
jul 32
 
6.2%
feb 20
 
3.9%
jun 17
 
3.3%
oct 15
 
2.9%
apr 9
 
1.7%
dec 9
 
1.7%
jan 2
 
0.4%
Other values (2) 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

day
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
sun
95 
fri
85 
sat
84 
mon
74 
tue
64 
Other values (2)
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfri
2nd rowtue
3rd rowsat
4th rowfri
5th rowsun

Common Values

ValueCountFrequency (%)
sun 95
18.4%
fri 85
16.4%
sat 84
16.2%
mon 74
14.3%
tue 64
12.4%
thu 61
11.8%
wed 54
10.4%

Length

2024-09-24T17:38:19.547685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T17:38:19.677363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sun 95
18.4%
fri 85
16.4%
sat 84
16.2%
mon 74
14.3%
tue 64
12.4%
thu 61
11.8%
wed 54
10.4%

Most occurring characters

ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
i 85
 
5.5%
r 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
i 85
 
5.5%
r 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
i 85
 
5.5%
r 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
i 85
 
5.5%
r 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

FFMC
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.644681
Minimum18.7
Maximum96.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:19.837011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.7
5-th percentile84.1
Q190.2
median91.6
Q392.9
95-th percentile95.1
Maximum96.2
Range77.5
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation5.5201108
Coefficient of variation (CV)0.060898343
Kurtosis67.066041
Mean90.644681
Median Absolute Deviation (MAD)1.3
Skewness-6.575606
Sum46863.3
Variance30.471624
MonotonicityNot monotonic
2024-09-24T17:38:20.001945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.1 28
 
5.4%
91.6 28
 
5.4%
91 22
 
4.3%
91.7 19
 
3.7%
93.7 16
 
3.1%
92.4 16
 
3.1%
92.5 15
 
2.9%
94.8 14
 
2.7%
92.9 12
 
2.3%
90.2 12
 
2.3%
Other values (96) 335
64.8%
ValueCountFrequency (%)
18.7 1
 
0.2%
50.4 1
 
0.2%
53.4 1
 
0.2%
63.5 2
0.4%
68.2 1
 
0.2%
69 1
 
0.2%
75.1 2
0.4%
79.5 3
0.6%
81.5 2
0.4%
81.6 4
0.8%
ValueCountFrequency (%)
96.2 2
 
0.4%
96.1 6
1.2%
96 2
 
0.4%
95.9 2
 
0.4%
95.8 1
 
0.2%
95.5 2
 
0.4%
95.2 7
1.4%
95.1 5
1.0%
95 1
 
0.2%
94.9 3
0.6%

DMC
Real number (ℝ)

HIGH CORRELATION 

Distinct215
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.87234
Minimum1.1
Maximum291.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:20.177379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile14.92
Q168.6
median108.3
Q3142.4
95-th percentile231.1
Maximum291.3
Range290.2
Interquartile range (IQR)73.8

Descriptive statistics

Standard deviation64.046482
Coefficient of variation (CV)0.57765969
Kurtosis0.20482178
Mean110.87234
Median Absolute Deviation (MAD)34.9
Skewness0.54749779
Sum57321
Variance4101.9519
MonotonicityNot monotonic
2024-09-24T17:38:20.347291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
1.9%
129.5 9
 
1.7%
142.4 8
 
1.5%
231.1 8
 
1.5%
137 7
 
1.4%
108.3 7
 
1.4%
126.5 7
 
1.4%
35.8 7
 
1.4%
108.4 7
 
1.4%
117.9 6
 
1.2%
Other values (205) 441
85.3%
ValueCountFrequency (%)
1.1 1
0.2%
2.4 1
0.2%
3 2
0.4%
3.2 1
0.2%
3.6 1
0.2%
3.7 1
0.2%
4.4 2
0.4%
4.6 1
0.2%
4.9 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
291.3 1
 
0.2%
290 4
0.8%
287.2 1
 
0.2%
284.9 1
 
0.2%
276.3 4
0.8%
273.8 2
0.4%
269.8 1
 
0.2%
266.2 1
 
0.2%
263.1 1
 
0.2%
253.6 1
 
0.2%

DC
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.94004
Minimum7.9
Maximum860.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:20.500391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile43.58
Q1437.7
median664.2
Q3713.9
95-th percentile795.3
Maximum860.6
Range852.7
Interquartile range (IQR)276.2

Descriptive statistics

Standard deviation248.06619
Coefficient of variation (CV)0.45272507
Kurtosis-0.24524352
Mean547.94004
Median Absolute Deviation (MAD)80.2
Skewness-1.1004451
Sum283285
Variance61536.835
MonotonicityNot monotonic
2024-09-24T17:38:20.656093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
745.3 10
 
1.9%
692.6 9
 
1.7%
698.6 8
 
1.5%
692.3 8
 
1.5%
601.4 8
 
1.5%
715.1 8
 
1.5%
686.5 7
 
1.4%
706.4 7
 
1.4%
764 7
 
1.4%
647.1 7
 
1.4%
Other values (209) 438
84.7%
ValueCountFrequency (%)
7.9 1
 
0.2%
9.3 1
 
0.2%
15.3 1
 
0.2%
15.5 1
 
0.2%
15.8 1
 
0.2%
16.2 2
0.4%
18.7 1
 
0.2%
25.6 3
0.6%
26.6 1
 
0.2%
28.3 2
0.4%
ValueCountFrequency (%)
860.6 1
 
0.2%
855.3 4
0.8%
849.3 1
 
0.2%
844 1
 
0.2%
825.1 4
0.8%
822.8 1
 
0.2%
819.1 2
0.4%
817.5 1
 
0.2%
812.1 2
0.4%
811.2 1
 
0.2%

ISI
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0216634
Minimum0
Maximum56.1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:20.804096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q16.5
median8.4
Q310.8
95-th percentile17
Maximum56.1
Range56.1
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.5594772
Coefficient of variation (CV)0.50539207
Kurtosis21.458037
Mean9.0216634
Median Absolute Deviation (MAD)2.1
Skewness2.5363253
Sum4664.2
Variance20.788832
MonotonicityNot monotonic
2024-09-24T17:38:20.946349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6 23
 
4.4%
7.1 21
 
4.1%
6.3 20
 
3.9%
7 17
 
3.3%
8.4 17
 
3.3%
6.2 16
 
3.1%
9.2 15
 
2.9%
7.5 14
 
2.7%
7.8 12
 
2.3%
9 12
 
2.3%
Other values (109) 350
67.7%
ValueCountFrequency (%)
0 1
 
0.2%
0.4 2
 
0.4%
0.7 1
 
0.2%
0.8 3
0.6%
1.1 1
 
0.2%
1.5 1
 
0.2%
1.8 1
 
0.2%
1.9 6
1.2%
2 1
 
0.2%
2.1 2
 
0.4%
ValueCountFrequency (%)
56.1 1
 
0.2%
22.7 1
 
0.2%
22.6 1
 
0.2%
21.3 1
 
0.2%
20.3 4
0.8%
20 2
 
0.4%
18 4
0.8%
17.9 3
0.6%
17.7 5
1.0%
17 7
1.4%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct192
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.889168
Minimum2.2
Maximum33.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:21.088922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.2
Q115.5
median19.3
Q322.8
95-th percentile27.9
Maximum33.3
Range31.1
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.8066253
Coefficient of variation (CV)0.30740503
Kurtosis0.13616551
Mean18.889168
Median Absolute Deviation (MAD)3.6
Skewness-0.33117224
Sum9765.7
Variance33.716898
MonotonicityNot monotonic
2024-09-24T17:38:21.238847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.6 8
 
1.5%
17.4 8
 
1.5%
15.4 7
 
1.4%
20.6 7
 
1.4%
19.1 6
 
1.2%
20.4 6
 
1.2%
15.9 6
 
1.2%
17.8 6
 
1.2%
20.8 6
 
1.2%
15.2 6
 
1.2%
Other values (182) 451
87.2%
ValueCountFrequency (%)
2.2 1
 
0.2%
4.2 1
 
0.2%
4.6 6
1.2%
4.8 1
 
0.2%
5.1 5
1.0%
5.2 1
 
0.2%
5.3 3
0.6%
5.5 1
 
0.2%
5.8 2
 
0.4%
6.7 1
 
0.2%
ValueCountFrequency (%)
33.3 1
 
0.2%
33.1 1
 
0.2%
32.6 1
 
0.2%
32.4 2
0.4%
32.3 1
 
0.2%
31 1
 
0.2%
30.8 2
0.4%
30.6 1
 
0.2%
30.2 3
0.6%
29.6 1
 
0.2%

RH
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.288201
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:21.396588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile24
Q133
median42
Q353
95-th percentile77
Maximum100
Range85
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.317469
Coefficient of variation (CV)0.36843829
Kurtosis0.43818286
Mean44.288201
Median Absolute Deviation (MAD)10
Skewness0.86290401
Sum22897
Variance266.2598
MonotonicityNot monotonic
2024-09-24T17:38:21.541620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 33
 
6.4%
39 24
 
4.6%
35 20
 
3.9%
42 17
 
3.3%
43 17
 
3.3%
45 16
 
3.1%
34 16
 
3.1%
40 15
 
2.9%
33 15
 
2.9%
46 14
 
2.7%
Other values (65) 330
63.8%
ValueCountFrequency (%)
15 2
 
0.4%
17 1
 
0.2%
18 1
 
0.2%
19 4
 
0.8%
20 1
 
0.2%
21 7
1.4%
22 5
 
1.0%
24 13
2.5%
25 10
1.9%
26 6
1.2%
ValueCountFrequency (%)
100 1
0.2%
99 1
0.2%
97 1
0.2%
96 1
0.2%
94 1
0.2%
90 2
0.4%
88 1
0.2%
87 1
0.2%
86 2
0.4%
84 1
0.2%

wind
Real number (ℝ)

Distinct21
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0176015
Minimum0.4
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:21.672568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.3
Q12.7
median4
Q34.9
95-th percentile7.6
Maximum9.4
Range9
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.7916526
Coefficient of variation (CV)0.44595079
Kurtosis0.054323817
Mean4.0176015
Median Absolute Deviation (MAD)1.3
Skewness0.57100113
Sum2077.1
Variance3.210019
MonotonicityNot monotonic
2024-09-24T17:38:21.793824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.1 53
10.3%
2.2 53
10.3%
4 51
9.9%
4.9 48
9.3%
2.7 44
8.5%
4.5 41
7.9%
5.4 41
7.9%
3.6 40
7.7%
1.8 31
 
6.0%
5.8 24
 
4.6%
Other values (11) 91
17.6%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.9 13
 
2.5%
1.3 14
 
2.7%
1.8 31
6.0%
2.2 53
10.3%
2.7 44
8.5%
3.1 53
10.3%
3.6 40
7.7%
4 51
9.9%
4.5 41
7.9%
ValueCountFrequency (%)
9.4 4
 
0.8%
8.9 1
 
0.2%
8.5 8
 
1.5%
8 5
 
1.0%
7.6 14
 
2.7%
7.2 4
 
0.8%
6.7 8
 
1.5%
6.3 19
3.7%
5.8 24
4.6%
5.4 41
7.9%

rain
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021663443
Minimum0
Maximum6.4
Zeros509
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:21.909442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.4
Range6.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29595912
Coefficient of variation (CV)13.661684
Kurtosis421.29596
Mean0.021663443
Median Absolute Deviation (MAD)0
Skewness19.816344
Sum11.2
Variance0.087591801
MonotonicityNot monotonic
2024-09-24T17:38:22.017541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 509
98.5%
0.2 2
 
0.4%
0.8 2
 
0.4%
1 1
 
0.2%
6.4 1
 
0.2%
0.4 1
 
0.2%
1.4 1
 
0.2%
ValueCountFrequency (%)
0 509
98.5%
0.2 2
 
0.4%
0.4 1
 
0.2%
0.8 2
 
0.4%
1 1
 
0.2%
1.4 1
 
0.2%
6.4 1
 
0.2%
ValueCountFrequency (%)
6.4 1
 
0.2%
1.4 1
 
0.2%
1 1
 
0.2%
0.8 2
 
0.4%
0.4 1
 
0.2%
0.2 2
 
0.4%
0 509
98.5%

area
Real number (ℝ)

ZEROS 

Distinct251
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.847292
Minimum0
Maximum1090.84
Zeros247
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-09-24T17:38:22.159564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.52
Q36.57
95-th percentile48.714
Maximum1090.84
Range1090.84
Interquartile range (IQR)6.57

Descriptive statistics

Standard deviation63.655818
Coefficient of variation (CV)4.9548043
Kurtosis194.14072
Mean12.847292
Median Absolute Deviation (MAD)0.52
Skewness12.846934
Sum6642.05
Variance4052.0632
MonotonicityNot monotonic
2024-09-24T17:38:22.322777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 247
47.8%
1.94 3
 
0.6%
0.43 2
 
0.4%
1.46 2
 
0.4%
1.95 2
 
0.4%
1.75 2
 
0.4%
1.64 2
 
0.4%
1.56 2
 
0.4%
1.63 2
 
0.4%
0.9 2
 
0.4%
Other values (241) 251
48.5%
ValueCountFrequency (%)
0 247
47.8%
0.09 1
 
0.2%
0.17 1
 
0.2%
0.21 1
 
0.2%
0.24 1
 
0.2%
0.33 1
 
0.2%
0.36 1
 
0.2%
0.41 1
 
0.2%
0.43 2
 
0.4%
0.47 1
 
0.2%
ValueCountFrequency (%)
1090.84 1
0.2%
746.28 1
0.2%
278.53 1
0.2%
212.88 1
0.2%
200.94 1
0.2%
196.48 1
0.2%
185.76 1
0.2%
174.63 1
0.2%
154.88 1
0.2%
105.66 1
0.2%

Interactions

2024-09-24T17:38:17.184478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:05.952162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.162031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.281563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.427708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.664145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.718962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.804043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.882730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.060118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.100590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.289581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.063607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.269631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.398357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.537864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.768262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.823148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.912266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.989188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.162848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.205659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.390165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.168838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.377715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.505389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.653787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.870088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.926912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.015721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.088202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.262885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.309896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.501764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.359746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.487772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.626407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.763430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.973492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.030112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.121404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.191587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.372563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.423823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.599992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.465799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.591839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.729581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.863065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.080228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.133929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.220234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.297235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.468743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.523566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.687899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.563356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.688414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.825043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.967923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.168189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.229015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.315495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.393575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.558868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.613192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.782068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.660645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.785043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.926467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.078062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.261614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.320108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.411329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.490688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.652584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.710262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.874185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.762601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.891978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.027178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.177649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.355461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.420014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.502853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.587252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.743651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.805240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.975958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.861408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.989710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.127538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.272935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.441667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.509824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.592930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.672456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.831424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.900630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:18.156382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:06.957197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.083069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.223771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.465904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.530640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.601557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.686990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.761574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:15.917433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.991431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:18.291656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:07.059815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:08.184607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:09.326254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:10.565598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:11.626520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:12.710328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:13.788689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:14.966436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:16.010305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T17:38:17.088387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-24T17:38:22.433787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
DCDMCFFMCISIRHXYareadaymonthraintempwind
DC1.0000.5590.2630.1040.026-0.073-0.1050.0620.0910.5540.0080.309-0.206
DMC0.5591.0000.5110.4250.035-0.0800.0050.0720.1080.4100.1210.503-0.110
FFMC0.2630.5111.0000.784-0.320-0.060-0.0100.0250.0880.4420.0970.595-0.035
ISI0.1040.4250.7841.000-0.177-0.011-0.0130.0120.1540.3050.1170.4160.136
RH0.0260.035-0.320-0.1771.0000.0660.050-0.0240.0710.1820.181-0.5180.037
X-0.073-0.080-0.060-0.0110.0661.0000.4930.0600.0000.1600.110-0.0510.027
Y-0.1050.005-0.010-0.0130.0500.4931.0000.0460.0410.1280.079-0.041-0.009
area0.0620.0720.0250.012-0.0240.0600.0461.0000.0290.000-0.0640.0790.053
day0.0910.1080.0880.1540.0710.0000.0410.0291.0000.0000.0500.0680.096
month0.5540.4100.4420.3050.1820.1600.1280.0000.0001.0000.0000.3370.225
rain0.0080.1210.0970.1170.1810.1100.079-0.0640.0500.0001.0000.0260.121
temp0.3090.5030.5950.416-0.518-0.051-0.0410.0790.0680.3370.0261.000-0.180
wind-0.206-0.110-0.0350.1360.0370.027-0.0090.0530.0960.2250.121-0.1801.000

Missing values

2024-09-24T17:38:18.428061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-24T17:38:18.648066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

XYmonthdayFFMCDMCDCISItempRHwindrainarea
075marfri86.226.294.35.18.2516.70.00.0
174octtue90.635.4669.16.718.0330.90.00.0
274octsat90.643.7686.96.714.6331.30.00.0
386marfri91.733.377.59.08.3974.00.20.0
486marsun89.351.3102.29.611.4991.80.00.0
586augsun92.385.3488.014.722.2295.40.00.0
686augmon92.388.9495.68.524.1273.10.00.0
786augmon91.5145.4608.210.78.0862.20.00.0
886septue91.0129.5692.67.013.1635.40.00.0
975sepsat92.588.0698.67.122.8404.00.00.0
XYmonthdayFFMCDMCDCISItempRHwindrainarea
50724augfri91.0166.9752.67.125.9413.60.00.00
50812augfri91.0166.9752.67.125.9413.60.00.00
50954augfri91.0166.9752.67.121.1717.61.42.17
51065augfri91.0166.9752.67.118.2625.40.00.43
51186augsun81.656.7665.61.927.8352.70.00.00
51243augsun81.656.7665.61.927.8322.70.06.44
51324augsun81.656.7665.61.921.9715.80.054.29
51474augsun81.656.7665.61.921.2706.70.011.16
51514augsat94.4146.0614.711.325.6424.00.00.00
51663novtue79.53.0106.71.111.8314.50.00.00

Duplicate rows

Most frequently occurring

XYmonthdayFFMCDMCDCISItempRHwindrainarea# duplicates
034augsun91.4142.4601.410.619.8395.40.00.002
136junfri91.194.1232.17.119.2384.50.00.002
243augwed92.1111.2654.19.620.4424.90.00.002
344marsat91.735.880.87.817.0274.90.028.662